add_noise_white — 2D `noise` op

• Data kinds: image → image

• Call: fullseye.apply(img, "add_noise_white", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

• HALCON equivalent: add_noise_white (the HALCON reference is a useful guide to its meaning and parameters)

Usage

Adds Gaussian white noise (generated with `np.random.default_rng, standard deviation 0.02+0.2*b). The random seed is deterministically derived from a via int(a*997)+7, so the same a always reproduces the same noise pattern (note that this is not truly random — a is used as a pseudo-knob that selects a "noise appearance type"). Corresponds to HALCON's add_noise_white` (Add noise to an image.).

> The detailed description below is the original text — the summary and the headings are translated.

`a は乱数シード(=ノイズパターン)を、b` はノイズの強さ(標準偏差)

を振る。両方が使われるが、`a` の意味は「強さ」ではなく「パターン」で

ある点が他の op と異なる。

Detailed usage guide

• gallery2d_smoothing_rank family guide

Background guides (the physics and conventions behind this op)

• mv_image_sensors — 産業用イメージセンサ（現行品中心）

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• gallery2d_smoothing_rank — py -3.11 examples/gallery2d_smoothing_rank.py

Ops the type connects to (they accept `image` as input)

identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter

Same category (`noise`)

add_noise_distribution

*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

add_noise_white — 2D noise op